After the low-level features are collected through the convolutional network, they are converted to gram matrices and are used for the transfer stage. Given the representation \(\mathbf {x}\)  of an input audio signal (waveform or spectrogram), a convolutional neural network architecture is used to extract statistics that characterize stationary sound textures.
Let \(F_{\ell }=[f_{{\ell },k}]_{k=1}^{N_\ell }\)  be activation vector
on layer \(\ell \)  with \(N_\ell \)  nodes.
Following the practice in [1]},
we used the Gram matrix \(G_{\ell }=F_\ell ^T F_\ell \)  as the style loss statistics,
and minimized a two-fold loss function
\(\mathcal {L}(\mathbf {x},\mathbf {x}_c,\mathbf {x}_s)=\alpha \mathcal {L}_c(\mathbf {x},\mathbf {x}_c)+\mathcal {L}_s(\mathbf {x},\mathbf {x}_s)\) 
